Can we predict sleep-disordered breathing in pregnancy? The clinical utility of symptoms

Authors

  • Danielle L. Wilson,

    Corresponding author
    1. Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia
    • Correspondence

      Danielle L. Wilson, Ground Floor Bowen Centre, Institute for Breathing and Sleep, Austin Health, Bowen Centre, Studley Road, Heidelberg, Vic. 3084, Australia. Tel.: 613-9496-3517; fax: 613-9496-5124; e-mail: danielle.wilson@austin.org.au

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  • Susan P. Walker,

    1. Department of Perinatal Medicine, Mercy Hospital for Women, Heidelberg, Vic., Australia
    2. Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Vic., Australia
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  • Alison M. Fung,

    1. Department of Perinatal Medicine, Mercy Hospital for Women, Heidelberg, Vic., Australia
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  • Fergal O'Donoghue,

    1. Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia
    2. Department of Medicine, University of Melbourne, Parkville, Vic., Australia
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  • Maree Barnes,

    1. Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia
    2. Department of Medicine, University of Melbourne, Parkville, Vic., Australia
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  • Mark Howard

    1. Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia
    2. Department of Medicine, University of Melbourne, Parkville, Vic., Australia
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Summary

Sleep-disordered breathing (SDB) is reported commonly during pregnancy and is associated with an increased risk of adverse maternal and fetal outcomes, but the majority of these data are based upon self-report measures not validated for pregnancy. This study examined the predictive value of screening questionnaires for SDB administered at two time-points in pregnancy, and attempted to develop an ‘optimized predictive model’ for detecting SDB in pregnancy. A total of 380 women were recruited from an antenatal clinic in the second trimester of pregnancy. All participants completed the Berlin Questionnaire and the Multivariable Apnea Risk Index (MAP Index) at recruitment, with a subset of 43 women repeating the questionnaires at the time of polysomnography at 37 weeks' gestation. Fifteen of 43 (35%) women were confirmed to have a respiratory disturbance index (RDI) > 5 h−1. Prediction of an RDI > 5 h−1 was most accurate during the second trimester for both the Berlin Questionnaire (sensitivity 0.93, specificity 0.50, positive predictive value 0.50 and negative predictive value 0.93), and the MAP Index [area under the receiver operating characteristic (ROC) curve of 0.768]. A stepwise selection model identified snoring volume, a body mass index (BMI)≥32 kg m−2 and tiredness upon awakening as the strongest independent predictors of SDB during pregnancy; this model had an area under the ROC curve of 0.952. We conclude that existing clinical prediction models for SDB perform inadequately as a screening tool in pregnancy. The development of a highly predictive model from our data shows promise for a quick and easy screening tool to be validated for future use in pregnancy.

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